Overview

Dataset statistics

Number of variables13
Number of observations119735
Missing cells13587
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.0 MiB
Average record size in memory437.8 B

Variable types

Text5
DateTime1
Numeric7

Alerts

close is highly overall correlated with pre_settle and 1 other fieldsHigh correlation
open_interest is highly overall correlated with turnover and 1 other fieldsHigh correlation
pre_settle is highly overall correlated with close and 1 other fieldsHigh correlation
settle is highly overall correlated with close and 1 other fieldsHigh correlation
turnover is highly overall correlated with open_interest and 1 other fieldsHigh correlation
volume is highly overall correlated with open_interest and 1 other fieldsHigh correlation
open has 4529 (3.8%) missing valuesMissing
high has 4529 (3.8%) missing valuesMissing
low has 4529 (3.8%) missing valuesMissing
close has 3524 (2.9%) zerosZeros
volume has 12864 (10.7%) zerosZeros
open_interest has 6918 (5.8%) zerosZeros
turnover has 12864 (10.7%) zerosZeros

Reproduction

Analysis started2024-03-21 02:22:39.420121
Analysis finished2024-03-21 02:22:54.179358
Duration14.76 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

symbol
Text

Distinct2943
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size7.1 MiB
2024-03-21T10:22:55.223868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5656408
Min length5

Characters and Unicode

Total characters666402
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa1103
2nd rowa1103
3rd rowa1103
4th rowa1103
5th rowa1103
ValueCountFrequency (%)
sc2109 691
 
0.6%
pm301 476
 
0.4%
oi309 472
 
0.4%
ri307 466
 
0.4%
wh307 466
 
0.4%
rs307 358
 
0.3%
fg305 338
 
0.3%
rm305 319
 
0.3%
jr403 312
 
0.3%
sr101 243
 
0.2%
Other values (2933) 115594
96.5%
2024-03-21T10:22:56.499393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 121787
18.3%
0 119894
18.0%
2 70708
 
10.6%
9 32235
 
4.8%
5 30459
 
4.6%
3 27942
 
4.2%
U 17218
 
2.6%
4 16721
 
2.5%
A 15318
 
2.3%
R 15063
 
2.3%
Other values (40) 199057
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 666402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 121787
18.3%
0 119894
18.0%
2 70708
 
10.6%
9 32235
 
4.8%
5 30459
 
4.6%
3 27942
 
4.2%
U 17218
 
2.6%
4 16721
 
2.5%
A 15318
 
2.3%
R 15063
 
2.3%
Other values (40) 199057
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 666402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 121787
18.3%
0 119894
18.0%
2 70708
 
10.6%
9 32235
 
4.8%
5 30459
 
4.6%
3 27942
 
4.2%
U 17218
 
2.6%
4 16721
 
2.5%
A 15318
 
2.3%
R 15063
 
2.3%
Other values (40) 199057
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 666402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 121787
18.3%
0 119894
18.0%
2 70708
 
10.6%
9 32235
 
4.8%
5 30459
 
4.6%
3 27942
 
4.2%
U 17218
 
2.6%
4 16721
 
2.5%
A 15318
 
2.3%
R 15063
 
2.3%
Other values (40) 199057
29.9%

date
Date

Distinct3203
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size935.6 KiB
Minimum2011-01-04 00:00:00
Maximum2024-03-11 00:00:00
2024-03-21T10:22:56.712468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:56.933419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open
Text

MISSING 

Distinct23610
Distinct (%)20.5%
Missing4529
Missing (%)3.8%
Memory size7.1 MiB
2024-03-21T10:22:57.882855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.9791851
Min length0

Characters and Unicode

Total characters688838
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9442 ?
Unique (%)8.2%

Sample

1st row4110.0
2nd row4122.0
3rd row4091.0
4th row4065.0
5th row4051.0
ValueCountFrequency (%)
0.0 8314
 
7.2%
3200.0 60
 
0.1%
2950.0 52
 
< 0.1%
4400.0 48
 
< 0.1%
3600.0 48
 
< 0.1%
4150.0 48
 
< 0.1%
8600.0 47
 
< 0.1%
3680.0 47
 
< 0.1%
4250.0 46
 
< 0.1%
3750.0 45
 
< 0.1%
Other values (23599) 106425
92.4%
2024-03-21T10:22:59.023866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 183429
26.6%
. 115180
16.7%
1 56318
 
8.2%
5 56030
 
8.1%
2 50557
 
7.3%
3 45403
 
6.6%
4 42599
 
6.2%
6 38466
 
5.6%
8 38092
 
5.5%
7 31943
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 688838
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 183429
26.6%
. 115180
16.7%
1 56318
 
8.2%
5 56030
 
8.1%
2 50557
 
7.3%
3 45403
 
6.6%
4 42599
 
6.2%
6 38466
 
5.6%
8 38092
 
5.5%
7 31943
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 688838
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 183429
26.6%
. 115180
16.7%
1 56318
 
8.2%
5 56030
 
8.1%
2 50557
 
7.3%
3 45403
 
6.6%
4 42599
 
6.2%
6 38466
 
5.6%
8 38092
 
5.5%
7 31943
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 688838
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 183429
26.6%
. 115180
16.7%
1 56318
 
8.2%
5 56030
 
8.1%
2 50557
 
7.3%
3 45403
 
6.6%
4 42599
 
6.2%
6 38466
 
5.6%
8 38092
 
5.5%
7 31943
 
4.6%

high
Text

MISSING 

Distinct24361
Distinct (%)21.1%
Missing4529
Missing (%)3.8%
Memory size7.1 MiB
2024-03-21T10:22:59.835125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.9829696
Min length0

Characters and Unicode

Total characters689274
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9669 ?
Unique (%)8.4%

Sample

1st row4135.0
2nd row4122.0
3rd row4091.0
4th row4095.0
5th row4074.0
ValueCountFrequency (%)
0.0 8314
 
7.2%
4800.0 42
 
< 0.1%
4200.0 42
 
< 0.1%
2850.0 39
 
< 0.1%
3650.0 38
 
< 0.1%
4750.0 36
 
< 0.1%
3450.0 35
 
< 0.1%
3630.0 35
 
< 0.1%
3700.0 34
 
< 0.1%
4140.0 34
 
< 0.1%
Other values (24350) 106531
92.5%
2024-03-21T10:23:00.854719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 173563
25.2%
. 115180
16.7%
5 56498
 
8.2%
1 54980
 
8.0%
2 50077
 
7.3%
4 46160
 
6.7%
3 45848
 
6.7%
8 39940
 
5.8%
6 39562
 
5.7%
9 34072
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 689274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 173563
25.2%
. 115180
16.7%
5 56498
 
8.2%
1 54980
 
8.0%
2 50077
 
7.3%
4 46160
 
6.7%
3 45848
 
6.7%
8 39940
 
5.8%
6 39562
 
5.7%
9 34072
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 689274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 173563
25.2%
. 115180
16.7%
5 56498
 
8.2%
1 54980
 
8.0%
2 50077
 
7.3%
4 46160
 
6.7%
3 45848
 
6.7%
8 39940
 
5.8%
6 39562
 
5.7%
9 34072
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 689274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 173563
25.2%
. 115180
16.7%
5 56498
 
8.2%
1 54980
 
8.0%
2 50077
 
7.3%
4 46160
 
6.7%
3 45848
 
6.7%
8 39940
 
5.8%
6 39562
 
5.7%
9 34072
 
4.9%

low
Text

MISSING 

Distinct24125
Distinct (%)20.9%
Missing4529
Missing (%)3.8%
Memory size7.1 MiB
2024-03-21T10:23:01.849671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.9765116
Min length0

Characters and Unicode

Total characters688530
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9584 ?
Unique (%)8.3%

Sample

1st row4110.0
2nd row4085.0
3rd row4091.0
4th row4048.0
5th row4051.0
ValueCountFrequency (%)
0.0 8314
 
7.2%
4100.0 37
 
< 0.1%
3450.0 37
 
< 0.1%
3780.0 37
 
< 0.1%
2300.0 36
 
< 0.1%
2530.0 35
 
< 0.1%
2550.0 34
 
< 0.1%
4150.0 34
 
< 0.1%
4180.0 34
 
< 0.1%
4350.0 33
 
< 0.1%
Other values (24114) 106549
92.5%
2024-03-21T10:23:03.076082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 177012
25.7%
. 115180
16.7%
1 58279
 
8.5%
5 56939
 
8.3%
2 52742
 
7.7%
3 47111
 
6.8%
4 43373
 
6.3%
6 40427
 
5.9%
8 37271
 
5.4%
7 32415
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 688530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177012
25.7%
. 115180
16.7%
1 58279
 
8.5%
5 56939
 
8.3%
2 52742
 
7.7%
3 47111
 
6.8%
4 43373
 
6.3%
6 40427
 
5.9%
8 37271
 
5.4%
7 32415
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 688530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177012
25.7%
. 115180
16.7%
1 58279
 
8.5%
5 56939
 
8.3%
2 52742
 
7.7%
3 47111
 
6.8%
4 43373
 
6.3%
6 40427
 
5.9%
8 37271
 
5.4%
7 32415
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 688530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177012
25.7%
. 115180
16.7%
1 58279
 
8.5%
5 56939
 
8.3%
2 52742
 
7.7%
3 47111
 
6.8%
4 43373
 
6.3%
6 40427
 
5.9%
8 37271
 
5.4%
7 32415
 
4.7%

close
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25318
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12628.627
Minimum0
Maximum390490
Zeros3524
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size935.6 KiB
2024-03-21T10:23:03.283195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile136
Q12375
median4370
Q39755
95-th percentile58493
Maximum390490
Range390490
Interquartile range (IQR)7380

Descriptive statistics

Standard deviation29293.248
Coefficient of variation (CV)2.3195909
Kurtosis32.76689
Mean12628.627
Median Absolute Deviation (MAD)2897
Skewness5.1954049
Sum1.5120886 × 109
Variance8.5809435 × 108
MonotonicityNot monotonic
2024-03-21T10:23:03.493038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3524
 
2.9%
400 414
 
0.3%
3121 99
 
0.1%
4070 93
 
0.1%
4100 90
 
0.1%
373.3 86
 
0.1%
3745 82
 
0.1%
3390 78
 
0.1%
3197 70
 
0.1%
1911 68
 
0.1%
Other values (25308) 115131
96.2%
ValueCountFrequency (%)
0 3524
2.9%
42.05 1
 
< 0.1%
42.4 1
 
< 0.1%
42.85 1
 
< 0.1%
43.1 1
 
< 0.1%
43.3 1
 
< 0.1%
44.05 1
 
< 0.1%
44.1 1
 
< 0.1%
44.3 1
 
< 0.1%
44.7 3
 
< 0.1%
ValueCountFrequency (%)
390490 1
< 0.1%
357920 1
< 0.1%
357810 1
< 0.1%
352930 1
< 0.1%
350000 1
< 0.1%
349080 1
< 0.1%
348200 1
< 0.1%
346020 1
< 0.1%
345280 1
< 0.1%
344890 1
< 0.1%

volume
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73073
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220428.39
Minimum0
Maximum11325298
Zeros12864
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size935.6 KiB
2024-03-21T10:23:03.945269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1722.5
median30860
Q3206017
95-th percentile1032260.8
Maximum11325298
Range11325298
Interquartile range (IQR)205294.5

Descriptive statistics

Standard deviation523805.59
Coefficient of variation (CV)2.3763073
Kurtosis68.788783
Mean220428.39
Median Absolute Deviation (MAD)30860
Skewness6.4771837
Sum2.6392993 × 1010
Variance2.7437229 × 1011
MonotonicityNot monotonic
2024-03-21T10:23:04.144025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12864
 
10.7%
2 1061
 
0.9%
4 745
 
0.6%
1 596
 
0.5%
6 517
 
0.4%
8 431
 
0.4%
10 364
 
0.3%
3 302
 
0.3%
14 272
 
0.2%
12 267
 
0.2%
Other values (73063) 102316
85.5%
ValueCountFrequency (%)
0 12864
10.7%
1 596
 
0.5%
2 1061
 
0.9%
3 302
 
0.3%
4 745
 
0.6%
5 212
 
0.2%
6 517
 
0.4%
7 181
 
0.2%
8 431
 
0.4%
9 133
 
0.1%
ValueCountFrequency (%)
11325298 1
< 0.1%
11215608 1
< 0.1%
10649982 1
< 0.1%
10298962 1
< 0.1%
10250286 1
< 0.1%
10220650 1
< 0.1%
10207570 1
< 0.1%
10040322 1
< 0.1%
9851786 1
< 0.1%
9812676 1
< 0.1%

open_interest
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78350
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178405.61
Minimum0
Maximum4832902
Zeros6918
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size935.6 KiB
2024-03-21T10:23:04.339013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12602
median45426
Q3181450
95-th percentile814721
Maximum4832902
Range4832902
Interquartile range (IQR)178848

Descriptive statistics

Standard deviation355776.66
Coefficient of variation (CV)1.9942011
Kurtosis25.848017
Mean178405.61
Median Absolute Deviation (MAD)45403
Skewness4.281394
Sum2.1361395 × 1010
Variance1.2657703 × 1011
MonotonicityNot monotonic
2024-03-21T10:23:04.541638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6918
 
5.8%
2 1504
 
1.3%
1 975
 
0.8%
4 843
 
0.7%
6 657
 
0.5%
8 418
 
0.3%
3 354
 
0.3%
10 318
 
0.3%
12 239
 
0.2%
16 237
 
0.2%
Other values (78340) 107272
89.6%
ValueCountFrequency (%)
0 6918
5.8%
1 975
 
0.8%
2 1504
 
1.3%
3 354
 
0.3%
4 843
 
0.7%
5 186
 
0.2%
6 657
 
0.5%
7 94
 
0.1%
8 418
 
0.3%
9 134
 
0.1%
ValueCountFrequency (%)
4832902 1
< 0.1%
4727336 1
< 0.1%
4711298 1
< 0.1%
4709702 1
< 0.1%
4577550 1
< 0.1%
4569212 1
< 0.1%
4552136 1
< 0.1%
4535336 1
< 0.1%
4533484 1
< 0.1%
4515004 1
< 0.1%

turnover
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105619
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6175243 × 108
Minimum0
Maximum1.8410021 × 1011
Zeros12864
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size935.6 KiB
2024-03-21T10:23:04.740649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120403.025
median901751.06
Q337248945
95-th percentile4.7699115 × 109
Maximum1.8410021 × 1011
Range1.8410021 × 1011
Interquartile range (IQR)37228542

Descriptive statistics

Standard deviation4.4653365 × 109
Coefficient of variation (CV)4.6429168
Kurtosis227.39624
Mean9.6175243 × 108
Median Absolute Deviation (MAD)901751.06
Skewness12.148178
Sum1.1515543 × 1014
Variance1.993923 × 1019
MonotonicityNot monotonic
2024-03-21T10:23:04.958840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12864
 
10.7%
3.2 12
 
< 0.1%
4.8 11
 
< 0.1%
4.2 7
 
< 0.1%
6.9 6
 
< 0.1%
7.56 5
 
< 0.1%
4.65 5
 
< 0.1%
11.83 5
 
< 0.1%
7.16 5
 
< 0.1%
4.51 5
 
< 0.1%
Other values (105609) 106810
89.2%
ValueCountFrequency (%)
0 12864
10.7%
1.24 1
 
< 0.1%
1.36 1
 
< 0.1%
1.37 2
 
< 0.1%
1.41 1
 
< 0.1%
1.71 1
 
< 0.1%
1.74 1
 
< 0.1%
1.77 1
 
< 0.1%
1.79 2
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
1.841002128 × 10111
< 0.1%
1.677204601 × 10111
< 0.1%
1.472793527 × 10111
< 0.1%
1.435022182 × 10111
< 0.1%
1.357605366 × 10111
< 0.1%
1.208361392 × 10111
< 0.1%
1.189071673 × 10111
< 0.1%
1.178495193 × 10111
< 0.1%
1.176288075 × 10111
< 0.1%
1.150501221 × 10111
< 0.1%

settle
Real number (ℝ)

HIGH CORRELATION 

Distinct25710
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12727.673
Minimum43
Maximum370800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size935.6 KiB
2024-03-21T10:23:05.162426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile365.9
Q12510
median4421
Q39787.5
95-th percentile58500
Maximum370800
Range370757
Interquartile range (IQR)7277.5

Descriptive statistics

Standard deviation29252.474
Coefficient of variation (CV)2.2983365
Kurtosis32.747699
Mean12727.673
Median Absolute Deviation (MAD)2687
Skewness5.1972063
Sum1.5239479 × 109
Variance8.5570726 × 108
MonotonicityNot monotonic
2024-03-21T10:23:05.360454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2479 422
 
0.4%
400 412
 
0.3%
3122 296
 
0.2%
2662 255
 
0.2%
801.4 250
 
0.2%
3317 176
 
0.1%
2360 135
 
0.1%
3043 133
 
0.1%
3198 113
 
0.1%
3121 105
 
0.1%
Other values (25700) 117438
98.1%
ValueCountFrequency (%)
43 1
< 0.1%
43.1 2
< 0.1%
43.25 1
< 0.1%
43.3 1
< 0.1%
43.35 1
< 0.1%
43.65 1
< 0.1%
44.05 1
< 0.1%
44.3 2
< 0.1%
44.35 1
< 0.1%
44.4 1
< 0.1%
ValueCountFrequency (%)
370800 1
< 0.1%
355130 1
< 0.1%
353120 1
< 0.1%
349200 1
< 0.1%
348660 1
< 0.1%
348290 1
< 0.1%
347770 1
< 0.1%
346050 1
< 0.1%
345830 1
< 0.1%
345820 1
< 0.1%

pre_settle
Real number (ℝ)

HIGH CORRELATION 

Distinct25699
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12726.37
Minimum43.1
Maximum370800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size935.6 KiB
2024-03-21T10:23:05.561134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum43.1
5-th percentile365.9
Q12510
median4420
Q39790
95-th percentile58500
Maximum370800
Range370756.9
Interquartile range (IQR)7280

Descriptive statistics

Standard deviation29240.82
Coefficient of variation (CV)2.297656
Kurtosis32.715664
Mean12726.37
Median Absolute Deviation (MAD)2686
Skewness5.1948672
Sum1.5237919 × 109
Variance8.5502556 × 108
MonotonicityNot monotonic
2024-03-21T10:23:05.759066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2479 421
 
0.4%
400 412
 
0.3%
3122 296
 
0.2%
2662 255
 
0.2%
801.4 250
 
0.2%
3317 174
 
0.1%
2360 137
 
0.1%
3043 132
 
0.1%
3198 112
 
0.1%
3121 106
 
0.1%
Other values (25689) 117440
98.1%
ValueCountFrequency (%)
43.1 2
< 0.1%
43.25 1
< 0.1%
43.35 1
< 0.1%
43.65 1
< 0.1%
44.05 1
< 0.1%
44.3 2
< 0.1%
44.35 1
< 0.1%
44.4 1
< 0.1%
44.7 1
< 0.1%
44.9 2
< 0.1%
ValueCountFrequency (%)
370800 1
< 0.1%
355130 1
< 0.1%
353120 1
< 0.1%
349200 1
< 0.1%
348660 1
< 0.1%
348290 1
< 0.1%
347770 1
< 0.1%
345830 1
< 0.1%
345820 1
< 0.1%
345120 1
< 0.1%
Distinct69
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
2024-03-21T10:23:06.447513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.7386562
Min length1

Characters and Unicode

Total characters208178
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
a 3203
 
2.7%
l 3203
 
2.7%
c 3203
 
2.7%
rb 3203
 
2.7%
m 3203
 
2.7%
p 3203
 
2.7%
v 3203
 
2.7%
y 3203
 
2.7%
au 3203
 
2.7%
zn 3203
 
2.7%
Other values (59) 87705
73.2%
2024-03-21T10:23:07.323891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 21612
 
10.4%
C 19265
 
9.3%
A 18521
 
8.9%
P 17442
 
8.4%
R 17275
 
8.3%
U 17218
 
8.3%
S 12154
 
5.8%
J 9818
 
4.7%
M 9605
 
4.6%
N 8671
 
4.2%
Other values (15) 56597
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 21612
 
10.4%
C 19265
 
9.3%
A 18521
 
8.9%
P 17442
 
8.4%
R 17275
 
8.3%
U 17218
 
8.3%
S 12154
 
5.8%
J 9818
 
4.7%
M 9605
 
4.6%
N 8671
 
4.2%
Other values (15) 56597
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 21612
 
10.4%
C 19265
 
9.3%
A 18521
 
8.9%
P 17442
 
8.4%
R 17275
 
8.3%
U 17218
 
8.3%
S 12154
 
5.8%
J 9818
 
4.7%
M 9605
 
4.6%
N 8671
 
4.2%
Other values (15) 56597
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 21612
 
10.4%
C 19265
 
9.3%
A 18521
 
8.9%
P 17442
 
8.4%
R 17275
 
8.3%
U 17218
 
8.3%
S 12154
 
5.8%
J 9818
 
4.7%
M 9605
 
4.6%
N 8671
 
4.2%
Other values (15) 56597
27.2%

delivery_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9090324
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size935.6 KiB
2024-03-21T10:23:07.493900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5040229
Coefficient of variation (CV)0.59299436
Kurtosis-1.2786073
Mean5.9090324
Median Absolute Deviation (MAD)4
Skewness-0.017293249
Sum707518
Variance12.278176
MonotonicityNot monotonic
2024-03-21T10:23:07.641563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 23987
20.0%
9 23637
19.7%
5 23082
19.3%
10 8869
 
7.4%
3 6764
 
5.6%
7 6057
 
5.1%
11 5559
 
4.6%
12 5120
 
4.3%
6 4838
 
4.0%
2 4683
 
3.9%
Other values (2) 7139
 
6.0%
ValueCountFrequency (%)
1 23987
20.0%
2 4683
 
3.9%
3 6764
 
5.6%
4 3522
 
2.9%
5 23082
19.3%
6 4838
 
4.0%
7 6057
 
5.1%
8 3617
 
3.0%
9 23637
19.7%
10 8869
 
7.4%
ValueCountFrequency (%)
12 5120
 
4.3%
11 5559
 
4.6%
10 8869
 
7.4%
9 23637
19.7%
8 3617
 
3.0%
7 6057
 
5.1%
6 4838
 
4.0%
5 23082
19.3%
4 3522
 
2.9%
3 6764
 
5.6%

Interactions

2024-03-21T10:22:51.991048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:44.515152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:45.740608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:47.129971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:48.306960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:49.548210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:50.806140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:52.172665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:44.706756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:45.922153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:47.316933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:48.485712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:49.740827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:50.980242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:52.320517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:44.869404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:46.077892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:47.477759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:48.655246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:49.957607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:51.141440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:52.477095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:45.058221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:46.241400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:47.647634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:48.853789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:50.130993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:51.316265image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:52.658024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:45.226388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:46.435886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:47.819767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:49.037651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:50.301379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:51.489626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:52.823118image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:45.394368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:46.615571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:47.995016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:49.221105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:50.465986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:51.654615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:52.981094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:45.559076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:46.977930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:48.155308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:49.388552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:50.651939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-21T10:22:51.818285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-21T10:23:07.769920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
closedelivery_monthopen_interestpre_settlesettleturnovervolume
close1.0000.0110.1450.9770.9770.4580.147
delivery_month0.0111.000-0.0850.0170.0170.032-0.080
open_interest0.145-0.0851.0000.1020.1020.6740.959
pre_settle0.9770.0170.1021.0001.0000.4210.103
settle0.9770.0170.1021.0001.0000.4210.103
turnover0.4580.0320.6740.4210.4211.0000.702
volume0.147-0.0800.9590.1030.1030.7021.000

Missing values

2024-03-21T10:22:53.246094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T10:22:53.605371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symboldateopenhighlowclosevolumeopen_interestturnoversettlepre_settlevarietydelivery_month
0a11032011-01-04 00:00:004110.04135.04110.04128.05.0108.020.624123.04117.0A3
1a11032011-01-05 00:00:004122.04122.04085.04091.018.0105.073.824101.04123.0A3
2a11032011-01-06 00:00:004091.04091.04091.04091.01.0105.04.094091.04101.0A3
3a11032011-01-07 00:00:004065.04095.04048.04048.016.099.065.144071.04091.0A3
4a11032011-01-10 00:00:004051.04074.04051.04064.013.090.052.864066.04071.0A3
5a11032011-01-11 00:00:004057.04060.04050.04050.04.090.016.224054.04066.0A3
6a11032011-01-12 00:00:004050.04070.04050.04070.014.077.056.914065.04054.0A3
7a11032011-01-13 00:00:004095.04099.04045.04081.014.076.057.164082.04065.0A3
8a11032011-01-14 00:00:004055.04088.04049.04085.011.074.044.754068.04082.0A3
9a11032011-01-17 00:00:004093.04100.04093.04095.012.077.049.174097.04068.0A3
symboldateopenhighlowclosevolumeopen_interestturnoversettlepre_settlevarietydelivery_month
119725EC24042024-02-27 00:00:001949.91949.91862.01878.020603.022532.039182785.41901.81974.3EC4
119726EC24042024-02-28 00:00:001886.01945.01886.01931.714012.021725.026855399.21916.61901.8EC4
119727EC24042024-02-29 00:00:001931.31938.01890.01904.012112.020964.023158144.01912.01916.6EC4
119728EC24042024-03-01 00:00:001800.01855.41758.01780.017734.019494.032155288.81813.21912.0EC4
119729EC24042024-03-04 00:00:001802.01857.01731.81831.018882.019601.033785562.61789.31813.2EC4
119730EC24042024-03-05 00:00:001850.01891.21795.01802.515594.019452.028614990.01835.01789.3EC4
119731EC24042024-03-06 00:00:001731.01818.51731.01781.613140.019038.023349780.01777.01835.0EC4
119732EC24042024-03-07 00:00:001802.01866.11781.41794.315582.019549.028451173.81825.91777.0EC4
119733EC24042024-03-08 00:00:001808.01840.61802.01824.08168.018932.014883729.61822.21825.9EC4
119734EC24042024-03-11 00:00:001829.51899.01806.21815.015704.019135.029215721.61860.41822.2EC4